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About This Role
At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever\-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.
Job Description
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Be a part of transformational change where integrity matters, success inspires and great teams collaborate and innovate. As the fifth\-largest bank in the United States, we’re one of the country's most respected, innovative, ethical and successful financial institutions. We’re looking for people who want more than just a job – they want to make a difference! U.S. Bank is seeking a Software Engineer who will contribute toward the success of our technology initiatives in our digital transformation journey.
This position will be responsible for the analysis, design, testing, development and maintenance of best in class software experiences. The candidate is a self\-motivated individual who can collaborate with a team and across the organization. The candidate takes responsibility of the software artifacts produced adhering to U.S. Bank standards in order to ensure minimal impact to the customer experience. The candidate will be adept with the agile software development lifecycle and DevOps principles.
Essential Responsibilities
- Contribute to designing, developing, testing, operating, and maintaining software and AI\-enabled products with guidance from senior engineers
- Develop production\-ready, testable code for assigned components, services, and AI workflows
- Assist in building and integrating Generative AI solutions, including Retrieval\-Augmented Generation (RAG) pipelines using vector databases
- Support the development and enhancement of agentic AI systems, such as task\-oriented AI agents that can plan, reason, retrieve information, and invoke tools under defined guardrails
- Follow established architectural patterns and best practices, considering scalability, reliability, performance, and cost when implementing AI\-enabled solutions
- Assist with troubleshooting, model/output quality issues, and root\-cause analysis for traditional software and AI components; propose fixes under guidance
- Make sound design and implementation decisions with customer and employee experience in mind; escalate risks and questions appropriately
- Incorporate feedback from code reviews and update implementations to meet engineering, security, and compliance standards
- Participate in code reviews (as author and reviewer) to learn and apply software and AI engineering best practices
- Follow compliance, risk, data privacy, and security best practices in all phases of product and AI solution development
- Learn and apply software reliability engineering (SRE) practices and AI evaluation techniques embedded in the team’s standards
- Stay curious about emerging technologies in GenAI, agentic frameworks, and vector search, contributing ideas, prototypes, and proofs of concept
- Contribute to a culture of innovation, collaboration, and continuous improvement
- Communicate progress, blockers, and risks early; collaborate with the team to deliver well\-scoped, incremental features
Basic Qualifications
Bachelor’s degree in computer science, Engineering, or related field, or equivalent practical experience
Six to eight years of relevant software engineering experience
Experience Should Include
- 10\+ years of overall software development experience in Python AND Java or other object\-oriented languages
- 3\+ years of leading software projects with enterprise level solutions
- 2 \-3 years of containerization \& orchestration (Docker and Kubernetes, Bedrock, LLMs and vector databases
- 2\-3 years of hands\-on experience with Generative AI use cases, including RAG architectures, prompt engineering, and evaluation approaches
- Experience building and integrating vector databases (e.g., FAISS, Pinecone, Weaviate, OpenSearch, Azure AI Search) for semantic search and AI\-powered retrieval
- Exposure to agentic AI concepts, such as multi\-step reasoning, tool invocation, workflow orchestration, and AI agents built using modern frameworks
- Practical experience building AI applications using LangChain and LangGraph, including the design and orchestration of complex agent workflows and retrieval\-augmented generation pipelines
- 2–3 years of experience building data\-driven APIs and services using Python
- Exposure to Knowledge Graph concepts and graph databases (e.g., Neo4j, TigerGraph) is a plus.
- Familiarity with modern UI frameworks (e.g., React) and how AI services integrate into user\-facing applications is a plus
- Working knowledge of Agile software development lifecycle and DevOps practices
- Understanding of how AI\-powered features impact user workflows, decision\-making, and trust
- Growing understanding of responsible AI principles, model limitations, and guardrails in regulated environments
- Ability to collaborate across engineering, product, data, and business teams
- Technical proficiency to help define and implement solution requirements for end users
- Ability to communicate processes, design decisions, and results with engineers, product owners, scrum masters, vendors, and stakeholders
- Solid understanding of algorithms, data structures, architectural design patterns, and best practices
- Strong analytical, problem\-solving, and learning mindset
\*\*\*This role requires working from a U.S. Bank location three (3\) or more days per week.\*\*\*
If there’s anything we can do to accommodate a disability during any portion of the application or hiring process, please refer to our disability accommodations for applicants.
Benefits:
Our approach to benefits and total rewards considers our team members’ whole selves and what may be needed to thrive in and outside work. That's why our benefits are designed to help you and your family boost your health, protect your financial security and give you peace of mind. Our benefits include the following:
- Healthcare (medical, dental, vision)
- Basic term and optional term life insurance
- Short\-term and long\-term disability
- Pregnancy disability and parental leave
- 401(k) and employer\-funded retirement plan
- Paid vacation (from two to five weeks depending on salary grade and tenure)
- Up to 11 paid holiday opportunities
- Adoption assistance
- Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law
Review our full benefits available by employment status here.
U.S. Bank is an equal opportunity employer. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, gender identity, disability or veteran status, and other factors protected under applicable law.
E\-Verify
U.S. Bank participates in the U.S. Department of Homeland Security E\-Verify program in all facilities located in the United States and certain U.S. territories. The E\-Verify program is an Internet\-based employment eligibility verification system operated by the U.S. Citizenship and Immigration Services.
The salary range reflects figures based on the primary location, which is listed first. The actual range for the role may differ based on the location of the role. In addition to salary, U.S. Bank offers a comprehensive benefits package, including incentive and recognition programs, equity stock purchase 401(k) contribution and pension (all benefits are subject to eligibility requirements). Pay Range: $133,365\.00 \- $156,900\.00
U.S. Bank will consider qualified applicants with arrest or conviction records for employment. U.S. Bank conducts background checks consistent with applicable local laws, including the Los Angeles County Fair Chance Ordinance and the California Fair Chance Act as well as the San Francisco Fair Chance Ordinance. U.S. Bank is subject to, and conducts background checks consistent with the requirements of Section 19 of the Federal Deposit Insurance Act (FDIA). In addition, certain positions may also be subject to the requirements of FINRA, NMLS registration, Reg Z, Reg G, OFAC, the NFA, the FCPA, the Bank Secrecy Act, the SAFE Act, and/or federal guidelines applicable to an agreement, such as those related to ethics, safety, or operational procedures.
Applicants must be able to comply with U.S. Bank policies and procedures including the Code of Ethics and Business Conduct and related workplace conduct and safety policies.
Posting may be closed earlier due to high volume of applicants.
Salary Context
This $133K-$156K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At U.S. Bank, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $133K to $156K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
U.S. Bank AI Hiring
U.S. Bank has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Chicago, IL, US, Irving, TX, US, Saint Paul, MN, US. Compensation range: $115K - $200K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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